AI Bank Statement Reconciliation

The month-end bottleneck nobody budgets for

Advanced bank reconciliation in Dynamics 365 Finance & Operations gives you a statement import and a matching rule engine, and then it hands the residue back to a human. In practice the residue is most of the work. A customer pays four invoices in one transfer with a reference that carries only their own internal number. The bank posts an FX conversion fee as a separate line. A card acquirer settles a batch net of commission. A payroll file arrives as one debit against thirty ledger postings. None of these match on amount-plus-date-plus-reference, so a treasury analyst opens the reconciliation worksheet and spends the first three days of every month clicking through unmatched lines in Cash and bank management.

The cost compounds. Bank reconciliation gates the ledger close, so every hour spent in the worksheet delays trial balance review, intercompany settlement and consolidation. And because the matching rules that ship with the product are exact-value rules, the more legal entities and bank accounts you add, the worse the ratio of auto-matched to manual lines becomes.

What ECOSIRE builds

We build an X++ extension package for Dynamics 365 Finance and Supply Chain Management. No overlayering — everything is delivered as extension classes, table extensions, form extensions and Chain of Command augmentations, so your environment stays on a supported upgrade path.

Statement ingestion that handles your banks' actual files

We extend the existing bank statement import framework and add the formats your banks really send: ISO 20022 CAMT.053 and CAMT.054, MT940 and MT942, plus bank-specific CSV and fixed-width layouts. Import runs through the Data management framework as a data entity, so you get the standard staging, error handling, and the option to schedule it as a recurring batch job or expose it over OData for a treasury workstation to push into. Files landing in SFTP or Azure Blob are polled by a batch job on your schedule.

A matching engine that scores instead of asserting

The core of the build is a scoring engine that runs after the native matching rules have taken their pass. Each unmatched statement line is scored against candidate open transactions — customer payments, vendor payments, deposit slips, ledger journal lines, bank fee entries — using a weighted combination of amount proximity, date window, reference and remittance-text similarity, counterparty name similarity and historical behaviour on that bank account. Lines above your confidence threshold post automatically; lines in the grey band land on a review worklist showing the top candidates and the reason each was scored the way it was. Nothing is a black box, and nothing posts silently.

One-to-many and many-to-one settlement

The engine proposes combinations: one statement credit against several open customer invoices, several statement lines against one vendor payment run, and a net settlement against gross amounts plus a commission line. Proposed splits respect your settlement rules and post through the standard settlement APIs so aging, discounts and write-off tolerance behave exactly as they do today.

Fees, FX differences and write-offs

Unmatched residual amounts inside your tolerance are posted to the ledger accounts and financial dimensions you configure per bank account and per difference type — bank charge, FX revaluation, rounding, short payment. Ledger dimension defaulting is configurable so the posting inherits the right department, cost centre or business unit rather than dropping into a suspense account somebody has to clear later.

Learning from what your team actually does

Every manual match a user makes is captured as a training signal. Where a reference pattern, a counterparty alias or a recurring fee description repeats, the engine raises its score for that pattern next month. Nothing is shipped pre-trained on other people's data — the model is your data, in your environment.

Workspace and controls

A reconciliation workspace surfaces open statements by legal entity and bank account, auto-match rate, aged unmatched lines and the review queue. Approval of grey-band matches can be routed through the standard workflow framework so a controller signs off before posting, with the full audit trail the auditors will ask for.

Who this is for

Organisations running several legal entities and more than a handful of bank accounts in Dynamics 365 F&O, where reconciliation volume has outgrown manual matching. It is particularly relevant where you take high-volume customer receipts, settle through card or marketplace acquirers, or run multi-currency accounts that generate constant FX difference lines.

How delivery works

1. Scoping call. We walk your bank accounts, statement formats, sample files and current match rates, and look at how your team clears the exceptions today. We ask for anonymised sample statements — the file is the specification.

2. Fixed quote. You get a written scope covering formats, entities, matching dimensions, posting rules and the acceptance criteria, at a fixed price. No hourly drift.

3. Build. We develop the extension in our own environment against your target platform and application version, with your sample files as the test corpus.

4. Install in test. We deploy a deployable package into your sandbox through LCS, configure it with you, and run your real statements through it side by side with your current process so you can measure the match rate before anything touches production.

5. Production. After your sign-off, the same package goes through your normal LCS release pipeline into production. We are on the call for the first close.

6. Support. A defined post-go-live support window covers defects, and we hand over full source so you are never locked to us.

This is build-to-order software. Nothing here is sitting in a download folder — the app is developed for your configuration after the quote is accepted, with a typical lead time of two to four weeks depending on the number of formats and entities in scope.